This document describes a wearable AI device that uses computer vision and speech synthesis to help blind individuals. The device uses a Raspberry Pi with a camera to perform three main functions: facial recognition using convolutional neural networks and linear discriminant analysis, optical character recognition (OCR) to convert text to speech using a text-to-speech system, and object detection. The facial recognition and text are conveyed to the blind user through a speaker. The system is designed to be portable and help blind people identify faces, read text, and detect objects to assist them in daily life.

1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 09 | Sep 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2121
WEARABLE AI DEVICE FOR BLIND
Mugesh yadav. R1, Dhivakar. G2, Mohamed Rizwan. T3, Aravindh. J4, Vishnupriyanka. R5
1,2,3,4Dept. of Bio Medical Engineering, Rajiv Gandhi College of Engineering and Technology, Puducherry, India
5Assistant professor, Dept. of Bio Medical Engineering, Rajiv Gandhi College of Engineering and Technology,
Puducherry, India
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Abstract - In this paper we proposes a method of camera
based AI system for blind. This AI system we proper has
three specification. Facial recognition, OCR based TTS
system, object detection. All the specification are conveyed to
blind through speaker. Face recognition specification works
in the convolution neural network (CNN), linear
discriminant analysis in which we use the above mentioned
pattern and network for the training of face. Which is
experienced using face libraries opencv.neural network is
the problem solving for face indentification in which we use
facial key point detector and gabor wavelet filtering
detection systems purpose classifier to perform the object
detection with high scoring points of the image are
considered for detection of object. It divides the image into
regions and bounding boxes for object detection.
Key word - Optical Character Recognition (OCR), Text-To-
Speech (TTS), Raspberry Pi, English text, Tesseract OCR
Engine, Convolutional Neural Network(CCN), Linear
discriminant analysis(LDA), Gabor filter, Feature
extraction.
I. INTRODUCTION
OCR is used to convert text into ASCII form. It has wide
scope in the field of research for the past few years. It gives
alphabetical recognition of a printed characters or image.
Most of the documents are in printed form of text such as
reports, instruction and newspaper etc. Blind people
cannot able to read newspaper, cannot identify currency
notes, etc thus they are struggling in day to day life.
AI is the emerging technology which is still under
research we use this AI for our proposed model for many
security purpose and use facial recognition as biometric
processes. In facial recognition we verify human face
database to know the particular purpose identity.
CCN has been used for face recognition character
recognition and object identification. An interestingly new
approach based on a convolutional neural architecture
which was presented in 2009 designed to detect highly
variable face pattern. Even though there are many system
lasting in object detection efficiency is less. We are trying
to improve the efficiency of the system.
II. LITERATURE SURVEY
In a statistical structure research, it calculates the
mathematical submission of noticed mixtures of extreme
at specified roles compared with other picture. The pixel
strength is categorized into first-order, second-order and
higher-order research works. The grayish level co-
occurrence matrix(GLCM) method is a way of getting
mathematical structure. A GLCM is a matrix where the
number of rows and columns is equal to the number of
gray levels, G in the image. The matrix element P(m,n |
∆x,∆y) is the relative frequency with two pixels, separated
by a distance (∆x,∆y), occur in given neighborhood.
One pixel offset is used as reference pixel of neighbor
. image pixel area is 0,0 occurs the number of times the left
cell is filled.
The wearable recognition system is faster ,more
responsive it should be prepared by advanced
microcontroller to reduce computer like compactness.PIC
is major factor offers development of system code using
setup.
There are many wearable aids for are used to assist
visually impaired people which divides into electronic
travel aids(ETAs),electronic orientation aids(EOAs),and
position locator devices(PLDs). Its all about individual
visually impaired people. This survey is all about various
past features and parameter f the performance its only for
the past reference point of view and not critizied n those
systems
III. FACIAL RECOGNITION
(A) linear discriminant analysis(lda)
LDA’s main purpose is for pattern recognition,
statistics which work in machine learning language to find
the linear combination of the feature to same character or
different one. It gathers new dimension which is high
separation of the projected class and has less variance.

2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 09 | Sep 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2122
Fig -1: linear discriminant analysis.
(B) CONVOLUTIONAL NEURAL NETWORK
LPG is used to extract the feature of human face as a
result it fetch input to the deep convolutional neural
network. It involves layer by layer by training network.
The core idea is to classify data of a sample at some
distance. Three convolutional network are used in this
network structure and in full connected layer two pooled
layer are used.
(C) convolutional layer
CNN is the deep class of neural network visual image
are analyzed most commonly it is multi colour perception.
The convolution layer is the main block in CNN. It goes
deeper when compared to other networks. The input and
output of the convolution neural network must be of same
size to a convolutional kernel of the convolutional layer.
Convolutional kernels has the content of the convolutional
layer. The convolution kernel has 5 size.
(D) gabor filter
The texture analysis using linear filter. It specific
frequency content of the image in a linear manner of
localized point analysis. It has the strong response of
structure of the image. It has the receptive field properties
of neurons.
(E) pooling layer
Pooling layer is called as descending or decreasing
layer. The pooling layer is classified into input layer,
hidden layer and output layer. It reduces the spatial size of
the representation and the amount of computation of the
network. It is based on discretization process.
Max pooling is maximum value of pixels in particularly
in small area.
IV. TEXT-TO-SPEECH
Our method has two modules: the first main process of
acquiring image from web cam and converting It into text
format using OCR(optical character recognition .the
second process is language processing and signal
processing converts text to speech using TTS (text-to-
speech)synthesizer.
Steps:
 Image acquisition by cam
 Getting the image into axial panel for creating
graphical user interface(GUI)
 Pre- processing the image (contrast leveling, RGB
to gray image, adapation Accordingly)
 Then preprocessed image is converted to words
that is text using OCR
 Then the text converts to speech which creates a
MP3 file which automatically reads using speaker
It also analysis using edge detectors and neural
networks.
V. OBJECT DETECTION
Object detection is one of the most challenging task.
The object detection is done using feature extraction.
(F) feature extraction
Instead of raw pixel data usage, feature extraction
provides easier classification. The image can be captured
through the camera. Then image recognition can be done
through features extracted from the data types of image
determines the types of feature extracted. Now the feature
extracted are in alpha numerical representation which can
be further processed in feature extraction mainly detects
the important area of the image and it is actual
representation of an image.
VI. HARDWARE REQUIREMENT
The main hardware requirements used for connecting
this system are:
 Raspberry pi 3 B+
 USB Web camera
 Speaker
 LCD
i. raspberry pi 3 b+
Raspberry pi 3 B+ is main component used in this
system. It is the final version of raspberry pi 3.

3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 09 | Sep 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2123
Specifications:
 1GB RAM
 1.4GHz 64-bit ARMv8.
 CSI port for connecting a camera.
 Micro SD port for loading operating system and
storing data.
Fig - 2: Raspberry pi
ii. usb web camera
USB web camera is used to capture the person’s image
for face recognition and for TTS system which captures
text images.
 24 mp camera.
 CMOS Light sensor.
 10x zoom.
Fig -3: Usb web cam
iii. speaker
Speaker is used to receive ouput in audio format.
iv. lcd screen
LCD screen is used to control the interface between
the specification in the system.
specifications:
 SPI interface.
 Colour LCD display.
 Pixel resolution 128x128.
Fig -4: Lcd screen
VII. SYSTEM DESCRIPTION
The project aims at the methods involved for detection in
log-gabor filters. In this system facial changes due to
variation in expression and pose can be easily obtained.
The whole face image are applied by global feature
extraction.
In this framework two main modules are involved.
The first stage involves images can be picked up by web
camera and then the images can be converted into text
document with the help of optical character recognition.
The second stage involves converting the digital images or
text document into text to speech synthesis(TTS). With the
help of the speaker the audio message can be clearly
identified.
Figure(5), shows the block diagram for facial
recognition detection and text to speech synthesis.
BLOCK DIAGRAM:
Fig -5: Block diagram
a) text to speech synthesis:
Speech synthesis is used in computer system.
Speech synthesis is first implemented in software and then
it is implemented in hardware. A text to speech converts
the text into speech. There are about 16 synthesized
speech can be created. For high quality output the storage
of entire words can be of specific usage domain.

4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 09 | Sep 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2124
Fig -6: Output of TTS, audio file generated.
b) face detection:
In the face detection the camera will easily find
the face. In an image the face can be easily identified.
Facial recognition can be easily extracted, cropped
and resized and then it is converted to gray scale .Gray
scale which is best to describe an image.
c) optical character recognition:
OCR follows a modification of an architecture. OCR
first stage is preprocessing. The stages of preprocessing is
image recognition. In the first part of object detection the
text, graphics and the other parts are separated. Feature
extraction is used to create the image, scaling and then the
line thickness of the image. The post-preprocessor is used
to improve accuracy of detection of an image.
Fig -7: Stimulation using raspberry pi(face recognition and
TTS)
VIII. RESULT AND DISCUSSION
In the detection of the facial image related to the
user it first compares the input image for facial with the
facial image which have been required for authentication.
The comparison is between 1x1. From the dataset to find
the user the face matching can be done. The comparison is
between 1xN.In this project we developed a device that
converts text images to speech. By using a facial
recognition method can be done. It is then implemented by
using Raspberry Pi. The series of image pre-processing is
done and it only contains the text, the background colour
will be removed. We are using two methods to convert the
images to speech. They are TTS system and OCR methods
has been used. The audio output is heard through the
speaker.
IX. CONCLUSION
This system helps the visually impaired individual helping
them identify faces from the trained face. This frame work
is portable and easily carried by the visually challenged
people. An individual can able to read the text by using the
TTS engine using optical character recognition.
X. FUTURE SCOPE
This system can be controlled using motion gesture. In
future we can also use this using like alexa, which works in
voice command. More compact format of the device.
XI. ACKNOWLEDGEMENT
The authors would likely to thanks Ms. R. Vishnu priyanka,
Assistant professor, Department of Bio-medical
engineering, at Rajiv Gandhi College of engineering and
technology for her kind support, constant encouragement
and development towards the project.
XII. REFERENCE
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"Image Tex-ture Feature Extraction Using GLCM
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5. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 09 | Sep 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2125
[5] Minjun wang, zhihui wang, jinlin li. Deep convolutional
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